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Issue No.08 - August (2001 vol.23)
pp: 890-895
ABSTRACT
<p><b>Abstract</b>—Biorthogonal wavelets are applied to parse multiaspect transient scattering data in the context of signal classification. A language-based genetic algorithm is used to design wavelet filters that enhance classification performance. The biorthogonal wavelets are implemented via the lifting procedure and the optimization is carried out using a classification-based cost function. Example results are presented for target classification using measured scattering data.</p>
INDEX TERMS
Genetic algorithms, wavelets, classification.
CITATION
Eric Jones, Paul Runkle, Nilanjan Dasgupta, Luise Couchman, Lawrence Carin, "Genetic Algorithm Wavelet Design for Signal Classification", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.23, no. 8, pp. 890-895, August 2001, doi:10.1109/34.946991
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